C ONCLUDING R EMARKS - c. Application Challenges

c. Application Challenges

V. C ONCLUDING R EMARKS

A comprehensive discussion of the economics surrounding healthcare utilization and the means by which governments, private insurers, and market dynamics themselves may constrain such utilization (and thus “ration” limited resources) would require far more attention than a single article could hope to achieve. Our goals here have indeed been more modest. We have focused on how certain tools of economics, mainly cost-effectiveness analysis, have shaped distinct types of non-price-rationing practices.

Indeed, much has been omitted in our discussion regarding the numerous other ways in which governments and private parties may regulate healthcare utilization. For instance, while we have addressed more categorical approaches to regulating which services patients may obtain, we have not addressed more case-by-case mechanisms that insurers

may employ—for example, utilization review. Nor have we discussed certain modern approaches to rationing care such as global payment systems that retreat from fee-for-service reimbursement models and instead reimburse providers (either individually or in a group) a flat fee for treating specified patients over a given period of time or during particular episodes of care.

Structures of this latter variety—that is, alternative means of reimbursing

physicians—are worth emphasizing briefly in these concluding remarks. Our discussion in this article largely focused on policies and programs aimed at curbing or shaping patient decision-making. Excessive use of medical care, however, may also arise from another source: physicians. Perhaps one of the most important features of the U.S.

healthcare system that may contribute to excessive healthcare utilization is the fee-for-service environment largely characterizing the U.S. healthcare delivery system.⁶⁸ Under a fee-for-service approach, physicians are effectively paid more for doing more, creating incentives on the part of physicians to perform an unnecessarily high degree of services, a phenomenon often labeled “physician induced demand.” Whether fee-for-service

reimbursement structures, in fact, cause physicians to provide an unnecessarily large number of procedures is the subject of an extensive literature.⁶⁹ While establishing causation in such studies is empirically challenging, the most convincing evidence put forth to date suggests that at least some physicians may indeed be inducing demand in this manner.⁷⁰ With these considerations in mind, more complete discussions regarding healthcare cost containment and healthcare rationing cannot ignore the decision-making role of physicians in this complex interaction between medical providers, insurers, and patients.

APPENDIX

Return to the heart attack example in Part II, in which we evaluated the use of two treatment interventions: streptokinase and t-PA. We now evaluate the choice between these two interventions employing benefit analysis (CBA), as opposed to cost-effectiveness analysis. Assume that the baseline treatment is streptokinase, and a policy-maker is deciding whether to adopt t-PA as a treatment for society, guided by CBA.

Effectively, this analysis entails calculating the monetary benefits of t-PA relative to that of streptokinase and asking whether such relative benefits exceed the costs of t-PA relative to streptokinase. If such net benefits are indeed greater than 0, the CBA suggests that efficiency would be enhanced by selecting t-PA over streptokinase.

Assume that the use of t-PA results in a lower risk of death than the use of streptokinase. How should such reduced risk of death be valued? CBAs often approach this inquiry by calculating something known as compensating variation (CV). In this context, CV can be thought of as the amount of money that must be taken away from the individual in order to leave her just as well off as she was prior to the reduction in risk.

Conversely, CV can be interpreted as the maximum amount of money the patient would be willing to pay to obtain the treatment reducing risk of death (t-PA) over the current treatment (streptokinase).

Continuing our example, suppose a representative heart attack patient has lifetime expected utility under treatment t of E U Y t

)

= 0, that is, the patient gets no utility if he or she is dead. Further assume that the patient has $100,000 in income.

Solving for CV means that we find the CV that leaves the consumer indifferent between an income of $100,000 with a probability of death of p and an income of $100,000–CV with a probability of death of q .

For concreteness, let’s solve a simple example by specifying a functional form for the utility function, say, u= ln

( )

Y , and assuming that Y = $100K, p = 0.05, and

0.025

q = .⁷¹ We can find CV = $25,562.⁷² In words, under the simplified example here, the representative heart attack patient’s willingness to pay for the t-PA treatment (as opposed to streptokinase) is $25,562, which far exceeds the monetary cost of the procedure over streptokinase ($1,800). If the relative monetary cost of the treatment ($1,800) equals the social opportunity cost of the treatment, the decision rule from the CBA would be to adopt t-PA as a treatment because it has positive net benefits.

Table 1. Cost-Effectiveness Analysis Example

1 Commentators have identified a range of normative frameworks to guide this welfare analysis. Such guiding principles include: (1) “first-come-first-serve” (assigning

priority to individuals simply on the basis of time), see American Thoracic Society, Fair Allocation of intensive Care Unit Resources, 156 AM.J.RESPIR. CRIT.CARE MED. 1282 (1997); but see Norman Daniels, Fair Process in Patient Selection for Antiretroviral Treatment in WHO’s Goal of 3 by 5, 366 L^ANCET 169 (2005); (2) “instrumental value” (assigning priority to patients who contribute more to society—e.g., giving healthcare professionals priority access to scarce vaccines given that these workers are needed to treat others; or assigning priority based on one’s responsibility for their medical need—e.g., providing lower priority to alcoholics), see Ezekiel Emanuel & Alan Wertheimer, Who Should Get Influenza Vaccine When Not All Can?, 312 SCI. 854 (2006); (3) “best outcomes”

(employing a utilitarian principle that aims to maximize social welfare, typically without regard to the distribution of benefits and burdens; this principle generally underlies the economic analyses used for healthcare rationing, see Part II); and (4)

“equity” (allocating scarce resources based on a fair distribution of benefits and burdens, rather than merely maximizing net benefits), see Alan Williams &

Richard Cookson, Equity in Health, in HANDBOOK OF HEALTH ECONOMICS 1863–

1910(Anthony Culyer & Joseph P. Newhouse eds., 2000). Selected equity principles include “prioritarianism” (giving priority to those who are worst off), see Derek Parfit, Equality and Priority, 10 RATIO 202 (1997),

“sufficientarianism” (giving priority to individuals or groups below a predefined threshold level of well-being), see Roger Crisp, Equality, Priority, and

Compassion, 113 E^THICS745 (2003), and Rawls’s “fair equality of opportunity principle” as applied to health (arguing that if there is a social obligation to

protect fair equality of opportunity, there is also a derivative obligation to promote health because health has special moral importance given its link with opportunity), see NORMAN DANIELS,JUST HEALTH (Cambridge University Press 2007); see also JÔHNRÂWLS,AT^{HEORY OF}JÛSTICE (Belknap Press 2nd ed.

1999). The above list of guiding principles roughly follows the taxonomy set forth in Govind Persad et al., Principles for Allocation of Scarce Medical Interventions, 373 LANCET 423 (2009); see also I. Glenn Cohen, Rationing Legal Services, 5 J.

LEGAL ANALYSIS 221 (2013).

2 See The Slow Rise of the Robot Surgeon, MITT^ECH.R^EV.(Mar. 24, 2010).

3 See Judd Kessler & Alvin Roth, Organ Allocation Policy and the Decision to Donate, 102 AM.ECON.REV. 2018 (2012).

4 See Adam Hofer et al., Expansion of Coverage under the Patient Protection and Affordable Care Act and Primary Care Utilization, 89 MILBANK QUARTERLY 69 (2011).

5 For recent research on various policy levers that can potentially be used to combat the shortage of human organs for transplant, see Eric Johnson & Daniel Goldstein, Do Defaults Save Lives?, 302 SCIENCE 1338 (2003); see also Judd Kessler & Alvin Roth, Loopholes Undermine Donation: An Experiment Motivated by an Organ Donation Priority Loophole in Israel, 114 J.PUB.ECON. 19 (2014).

6 While it is beyond the scope of this article, we note that dynamic allocation decisions of this nature implicate matters beyond just rationing and cost containment—mainly, innovation policy. There is an inevitable conflict between healthcare rationing policies that aim to constrain spending on the one hand, and healthcare innovation

policies that seek to promote research and development on the other. For instance, healthcare utilization of new technologies may sometimes appear to be inefficient and excessive from a static perspective, perhaps arising from moral hazard, physician-induced demand, or related features of healthcare systems. However, from a more dynamic perspective, the promise of such financial returns may stimulate the desire to produce such innovations in the first place. In fact, a leading explanation for growth in U.S. healthcare spending is technological innovation. See Amitabh Chandra & Jonathan Skinner, Technology Growth and Expenditure Growth in Health Care, 50 J.E^CON.L^IT.645 (2012).

7 See ANTHONY BOARDMAN ET AL.,COST–BENEFIT ANALYSIS (4th ed. 2011).

8 See Micah Hartman et al., National Health Spending in 2013: Growth Slows, Remains in Step with the Overall Economy, 34 HEALTH AFF. 1 (2014).

9 Determining the optimal amount of resources to healthcare, as any good or service, depends on, among other things, societal preferences, national wealth, and the distribution of that wealth. Contrary to public perception, increases in health spending does not necessarily imply decreases in social welfare or even create a (greater) need for rationing. Naturally, as we become richer, society’s willingness to pay for a longer and healthier life increases, which translates into greater healthcare spending overall and perhaps also as a percentage of national income.

As a result, some preconceived amount of national wealth devoted to healthcare, whether it be 18% or even 50%, does not serve as a sufficient condition for which to justify the enactment of policies aimed at rationing healthcare. See Robert Hall

& Charles Jones, The Value of Life and the Rise in Health Spending, 122 Q.J.

ECON. 39 (2007).

10 For a recent literature review on the ways CBA and CEA have been converging, see Linda Ryen & Mikael Svensson, The Willingness to Pay for a Quality Adjusted Life Year: A Review of the Empirical Literature, HEALTH ECON. (2014)

(forthcoming).

11 Nicholas Kaldor, Welfare Propositions of Economics and Interpersonal Comparisons of Utility, 49 ECON.J. 549 (1939); John Hicks, The Valuation of the Social Income, 7 E^CONOMICA 105 (1940).

12 The necessity of monetizing the benefits of healthcare interventions in CBAs arises because of the need to place a value on opportunity costs used to decide where to employ resources. See Alan Garber & Charles Phelps, Economic Foundations of Cost-Effectiveness Analysis, 16 J.HEALTH ECON.1 (1997). Any discussion of CBAs for health interventions naturally leads to the controversial methods of valuing health and life, which are far from similar. See W. Kip Viscusi, The Value of Individual and Societal Risks to Life and Health, in HANDBOOK OF THE

ECONOMICS OF RISK AND UNCERTAINTY 385 (Mark Machina & Kip Viscusi, 2014). The most frequently used terminology for valuing probabilistic decreases to risks to life is called the “value of statistical life” (VSL), which is simply a convenient way of summarizing one’s willingness to pay for small reductions in fatal risks. See W. Kip Viscusi & Joseph Aldy, The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World, 27 J.R^ISK&

UNCERTAINTY 5 (2003). For clarity, VSL is neither the value of saving the life of

a specific identified person nor the value of reducing high probabilities of death, for instance, from 1 to 0. Because many of those health interventions that we associate with healthcare rationing deal with large reductions in risk to life, VSL does not lend itself well to a CBA and should be avoided.

13 See Donald Kenkel, Using Estimates of the Value of a Statistical Life in Evaluating Consumer Policy Regulations, 26 J.CONSUMER POL’Y 1 (2003).

14 See Jeremiah Hurley, Chapter 2: An Overview of the Normative Economics of the Health Sector, in HANDBOOK OF HEALTH ECONOMICS 55–118 (Anthony Culyer &

Joseph Newhouse eds., 2000); Nien-he Hsieh et al., The Numbers Problem, 34 PHIL.&PUB.AFF.352 (2006); Joseph Raz, Numbers, With and Without

Contractualism, 16 RATIO 346 (2003).

15 See Magnus Johannesson, Should We Aggregate Relative or Absolute Changes in QALYs?, 10 HEALTH ECON. 573 (2001).

16 See BOARDMAN ET AL.,C^OST–B^ENEFITA^NALYSIS, at 464.

17 Cost-effectiveness analyses that use QALYs or other utility-based measures are sometimes referred to as “cost utility analyses” because the measures serve as a proxy for changes in utility levels. DALYs can be distinguished from QALYs in how “the mortality and morbidity burdens of various diseases” differ in different populations. Christopher Murray & Arnab Acharya, Understanding DALYs, 16 J.

HEALTH ECON. 703 (1997).

18 This example was modified from an example presented in BOARDMAN ET AL.,COST– B^ENEFITA^NALYSIS.

19 The incremental effectiveness is calculated by differencing the expected number of

heart attack survivors with the streptokinase treatment [(1–0.05)1000 = 950] with the expected number of heart attack survivors with no treatment [(1–0.1)1000 = 900].

20 The ICER is again calculated by dividing the incremental costs of $1.8 million

[(1000)($2000)–(1000)($200)] by the incremental effectiveness of 25 individuals [(1–0.05)1000–(1–0.025)1000].

21 See Christopher McCabe et al., The NICE Cost-Effectiveness Threshold: What It Is and What That Means, 26 PHARMACOECONOMICS 733 (2008).

22 For a discussion of the various ways in which QALYs have been valued, see Donald Kenkel, A WTP- and QALY-Based Approaches to Valuing Health for Policy:

Common Ground and Disputed Territory, 34 ENVTL.&RES.ECON. 419 (2006).

23 See id.

24See Susan Bartlett Foote & Peter Neumann, The Impact of Medicare Modernization on Coverage Policy: Recommendations for Reform, 11 AM.J.MANAGED CARE 140 (2005). Medicare has made several attempts to incorporate some form of CEA into its coverage considerations, but it has been unable to do so due to political opposition. See id.

25 See Sean Tunis, Why Medicare Has Not Established Criteria for Coverage Decisions, 350 NEW ENG.J.MED. 2196 (2004).

26 For concerns with HDHPs, see J. Frank Wharam et al., High-Deductible Insurance:

Two-Year Emergency Department and Hospital Use, 17 A^M.J.M^ANAGEDC^ARE 410 (2011). Regarding growth, 38% of covered workers were in HDHPs in 2013

and enrollment may accelerate under the Affordable Care Act. See Amelia Haviland et al., Growth of Consumer-Directed Health Plans to One-Half of All Employer-Sponsored Insurance Could Save $57 Billion Annually, 31 HEALTH

A^FF. 1009 (2012).

27 See Dana Goldman et al., Pharmacy Benefits and the Use of Drugs by the Chronically Ill, 291 J.AM.MED.ASS’N 2344 (2004).

28 See Kevin Frick & Michael Chernew, Beneficial Moral Hazard and the Theory of the Second Best, 46 INQUIRY:J.HEALTH CARE ORG.,PROVISION,&FIN.229 (2009).

29 See Amelia Haviland et al., How Do Consumer-Directed Health Plans Affect Vulnerable Populations?, 14 F. FOR HEALTH ECON.&POL’Y 1 (2011).

30 See Niteesh Choudhry et al., Assessing the Evidence for Value-Based Insurance Design, 29 HEALTH AFF. 1988 (2010).

31 See A. Mark Fendrick et al., A Benefit-Based Copay for Prescription Drugs: Patient Contribution Based on Total Benefits, Not Drug Acquisition Cost, 7 A^M.J.

MANAGED CARE 861 (2001). For example, a VBID plan offered by the University of Michigan lowers copayments for medications to treat diabetes, high blood pressure, high cholesterol, and depression as follows: generics by 100%, tier 2 by 50%, and tier 3 by 25%. See Alicen Spaulding et al., A Controlled Trial of Value-Based Insurance Design—The MHealthy: Focus on Diabetes (FOD) Trial, 4 IMPLEMENT SCI. 19 (2009).

32 Inhaled steroids, for example, are effective for treating asthma but not chronic obstructive pulmonary disease. See Choudhry et al., Assessing the Evidence for Value-Based Insurance Design.

33 See John Rowe et al., The Effect of Consumer-Directed Health Plans on the Use of

Preventive and Chronic Illness Services, 27 HEALTH AFF. 113 (2008).

34 See Sheila Reiss et al., Effect of Switching to a High-Deductible Health Plan on Use of Chronic Medications, 46 HÊALTHSÊRV.RÊS. 1382 (2011).

35 See Choudhry et al., Assessing the Evidence for Value-Based Insurance Design.

36 Impact of Decreasing Copayments on Medication Adherence Within a Disease Management environment, 27 HEALTH AFF. 103 (2008).

37 See Matthew B. Frank et al., The Effect of a Large Regional Health Plan’s Value-Based Insurance Design Program on Statin Use, 50 M^ED.C^ARE934 (2012).

38 See Teresa Gibson et al., Value-Based Insurance Plus Disease Management Increased Medication Use and Produced Savings, 30 HEALTH AFF. 100 (2011).

39 See Jon Kingsdale, After the False Start—What Can We Expect from the New Health Insurance Marketplaces?, 370 NEW ENG.J.MED 393 (2014).

40 See James Robinson, Hospital Tiers in Health Insurance: Balancing Consumer Choice with Financial Incentives, 22 HEALTH AFF. 135 (2003). Some insurers have started to experiment with stronger incentives to steer patients toward preferred providers, including larger cost-sharing amounts and greater inter-tier cost sharing differences. See id. To the extent that provider networks steer patients toward higher value providers, limited networks have the potential to be more effective than tiered networks because of their more severe incentive structure. On the other hand, relative to limited network plans, tiered network plans preserve patients’ ability to choose from a wide range of providers (albeit with higher cost sharing for nonpreferred providers) and thus could be more palatable to patients.

41 See Choudhry et al., Assessing the Evidence for Value-Based Insurance Design.

42 Mass. Med. Soc’y et al. v. Group Ins. Comm’n et al., No. # 2008-cv-2124 (Suffolk Sup. Ct., filed May 21, 2008); Wash. St. Med. Ass’n et al. v. Regence Blue Shield, No. # 06-2-30655-ISEA (King County Sup. Ct. 2006); Fairfield County Med. Soc’y et al. v. CIGNA Corp. et al., No. # CV-075002943 (Conn. Sup. Ct.

2008).

43 See Matthew Frank et al., The Impact of a Tiered Network on Hospital Choice, HEALTH SERVICES RESEARCH, in press (2015).

44 These preferences are elicited using a range of valuation techniques, including standard gamble, time tradeoff, rating scale, multi-attribute utility, and person tradeoff techniques. See Milton Weinstein et al., QALYs: The Basics, 12 VALUE IN

HEALTH S5 (2009).

45 See Dan Brock, Ethical Issues in the Use of Cost Effectiveness Analysis for the Prioritization of Health Resources, inHANDBOOK OF B^IOETHICS 294 (George Khushf,2004).

46 See MYRIAM HUNINK &PAUL GLASZIOU,DECISION MAKING IN HEALTH AND

MEDICINE:INTEGRATING EVIDENCE AND VALUES 268–269 (2001).

47 See Paul Menzel et al., The Role of Adaptation to Disability and Disease in Health State Valuation: A Preliminary Normative Analysis, 55 SOC.SCI.&MEDICINE

2149 (2002).

48 See Brock, Ethical Issues in the Use of Cost Effectiveness Analysis.

49 See Alan Williams, Intergenerational Equity: An Exploration of the “Fair Innings”

Argument, 6 HEALTH ECON.117 (1997).

50 See JOHN HARRIS,THE VALUE OF LIFE:AN INTRODUCTION TO MEDICAL ETHICS 87–102

(1990).

51 See NORMAL DANIELS,AM IMY PARENTS’KEEPER?AN ESSAY ON JUSTICE BETWEEN THE YOUNG AND THE O^LD (1988).

52 See Greg Bognar, Age Weighting, 24 ECON.&PHIL. 167 (2008).

53 See Anupam Jena & Thomas Philipson, Cost Effectiveness as a Price Control, 26 HEALTH AFF. 696 (2007).

54 See Bradley Herring, Suboptimal Provision of Preventive Healthcare Due to Expected Enrollee Turnover Among Private Insurers, 19 H^EALTHE^CON.438 (2010).

55 See Brock, Ethical Issues in the Use of Cost Effectiveness Analysis.

56 See FRANCIS KAMM,MORALITY,MORTALITY:DEATH AND WHOM TO SAVE FROM IT

369(1993).

57 See Brock, Ethical Issues in the Use of Cost Effectiveness Analysis.

58 See Emmett Keeler & Shan Cretin, Discounting of Life-Saving and Other Nonmonetary Effects, 29 MGMT.SCI.300 (1983).

59 See Norman Daniels, Four Unsolved Rationing Problems A Challenge, 24 HASTINGS

CENTER REP. 27 (1994).

60 This issue may also arise in other healthcare rationing contexts in determining the allocation of physically scarce resources such as solid organs and units of vaccine.

61 Some may argue that patients with condition Y already had a fair chance and lost in the

“natural lottery,” while others (e.g., luck egalitarians) want to compensate for bad brute luck.

62 See John Broome, Selecting People Randomly, 95 ETHICS 38 (1984).

In document The Economics of Healthcare Rationing (Page 26-40)